{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We're gonna count the topics of the sentences assigned to the n clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I1229 13:09:51.044848 139999523268416 file_utils.py:40] PyTorch version 1.1.0 available.\n"
     ]
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load pretrained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I1229 13:09:54.511405 139999523268416 SentenceTransformer.py:29] Load pretrained SentenceTransformer: bert-base-nli-mean-tokens\n",
      "I1229 13:09:54.514040 139999523268416 SentenceTransformer.py:32] Did not find a / or \\ in the name. Assume to download model from server\n",
      "I1229 13:09:54.618808 139999523268416 SentenceTransformer.py:68] Load SentenceTransformer from folder: /home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip\n",
      "I1229 13:09:54.695612 139999523268416 configuration_utils.py:154] loading configuration file /home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT/config.json\n",
      "I1229 13:09:54.720070 139999523268416 configuration_utils.py:174] Model config {\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"finetuning_task\": null,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"is_decoder\": false,\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"num_labels\": 2,\n",
      "  \"output_attentions\": false,\n",
      "  \"output_hidden_states\": false,\n",
      "  \"output_past\": true,\n",
      "  \"pruned_heads\": {},\n",
      "  \"torchscript\": false,\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_bfloat16\": false,\n",
      "  \"vocab_size\": 30522\n",
      "}\n",
      "\n",
      "I1229 13:09:54.725053 139999523268416 modeling_utils.py:390] loading weights file /home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT/pytorch_model.bin\n",
      "I1229 13:10:02.990906 139999523268416 tokenization_utils.py:311] Model name '/home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT' not found in model shortcut name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese, bert-base-german-cased, bert-large-uncased-whole-word-masking, bert-large-cased-whole-word-masking, bert-large-uncased-whole-word-masking-finetuned-squad, bert-large-cased-whole-word-masking-finetuned-squad, bert-base-cased-finetuned-mrpc, bert-base-german-dbmdz-cased, bert-base-german-dbmdz-uncased). Assuming '/home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT' is a path or url to a directory containing tokenizer files.\n",
      "I1229 13:10:02.992225 139999523268416 tokenization_utils.py:340] Didn't find file /home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT/tokenizer_config.json. We won't load it.\n",
      "I1229 13:10:02.993050 139999523268416 tokenization_utils.py:376] loading file /home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT/vocab.txt\n",
      "I1229 13:10:02.993776 139999523268416 tokenization_utils.py:376] loading file /home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT/added_tokens.json\n",
      "I1229 13:10:02.994529 139999523268416 tokenization_utils.py:376] loading file /home/saul/.cache/torch/sentence_transformers/public.ukp.informatik.tu-darmstadt.de_reimers_sentence-transformers_v0.2_bert-base-nli-mean-tokens.zip/0_BERT/special_tokens_map.json\n",
      "I1229 13:10:02.995230 139999523268416 tokenization_utils.py:376] loading file None\n",
      "I1229 13:10:03.037774 139999523268416 SentenceTransformer.py:89] Use pytorch device: cpu\n"
     ]
    }
   ],
   "source": [
    "model = SentenceTransformer('bert-base-nli-mean-tokens')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.manifold import TSNE\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#parameters\n",
    "MIN = 1 ### Also for easy mapping from clean sentence to original sentence in df\n",
    "STOPWORDS = True\n",
    "SIZE = 10000\n",
    "DIM = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/reddit_batch1/batch.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.iloc[0:SIZE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn = df['topic'].unique().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = set(stopwords.words('english'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for transformer\n",
    "def clean_sen_trans(sent):\n",
    "    clean_list = ''\n",
    "    if STOPWORDS == True:\n",
    "        for word in sent.split():\n",
    "           # print(word)\n",
    "            if word not in stop_words:\n",
    "                word = re.sub('[\\W_]+','',word)\n",
    "                word = re.sub(' +', ' ', word)\n",
    "                clean_list +=word.lower()+str(' ')\n",
    "    else:\n",
    "        for word in sent.split():\n",
    "            word = re.sub('[\\W_]+','',word)\n",
    "            word = re.sub(' +', ' ', word)\n",
    "            clean_list +=word.lower()+str(' ')\n",
    "\n",
    "    return clean_list\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre1= []\n",
    "for i in df.sentence:\n",
    "    clean = clean_sen_trans(i)\n",
    "    if len(clean) > MIN:\n",
    "        pre1.append(clean)\n",
    "       \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pre1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Batches: 100%|██████████| 1250/1250 [10:30<00:00,  2.13s/it]\n"
     ]
    }
   ],
   "source": [
    "embeddings = model.encode(pre1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['kid part parents would say give real reason cry ', 'doubt professional archaeologist writes research interests include finding gold resume however another question get asked time fact excavating unit carefully dig squares circles tightly controlled levels like pealing back layers history passerbys almost always ask searching find gold fascination shiny valuable metal universal think nevertheless archaeology finding gold cool find often context small bead flake like elaborate gold cups masks see movies would argue though treasure consider fragment past whether broken ceramic arrowhead bone needle valuable piece past ', 'thank congrats best wishes february ', 'bad juju ', 'bee ftfy ', 'morelia patzcuaro lovely hope check uruapan back mexico shame situation parts guerrero hope things improve around mexico great place good luck peru ', 'earliest would guess lsd legal therapeutic settings ', 'really means heard parents say kid screaming hitting makes sense mom definitely trying say thank anything little shit ']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Batches: 100%|██████████| 1/1 [00:01<00:00,  1.53s/it]\n"
     ]
    }
   ],
   "source": [
    "queries=[]\n",
    "for i in range(nn):\n",
    "    queries.append(pre1[np.random.randint(0,len(pre1))])\n",
    "print(queries)\n",
    "query_embeddings = model.encode(queries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kid part parents would say give real reason cry 0.9999999999999999\n",
      "really means heard parents say kid screaming hitting makes sense mom definitely trying say thank anything little shit 0.7852066151205668\n",
      "kid going cry regardless least take chance learn lesson 0.784442743858913\n",
      "idk love parents call kids assholes 0.7612920530967622\n",
      "kid knew babies came 3 asked told comes every big deal one embarrassed think 7 little old know nothing also think teacher way base 0.74392893728614\n",
      "doubt professional archaeologist writes research interests include finding gold resume however another question get asked time fact excavating unit carefully dig squares circles tightly controlled levels like pealing back layers history passerbys almost always ask searching find gold fascination shiny valuable metal universal think nevertheless archaeology finding gold cool find often context small bead flake like elaborate gold cups masks see movies would argue though treasure consider fragment past whether broken ceramic arrowhead bone needle valuable piece past 0.999999999999975\n",
      "another tough question u craxnehcark worked national geographic vicariously lived archaeologists across globe us trained archaeologist worked across different continents know standards rules equipment differ everywhere heck methods might change depending research question example might use smaller mesh screen looking ancient seeds moving stone blocks better understand pyramid use screen screen infrequently big mesh us rpa registry professional archaeologists something called skills passport uk global standardization though one region produces best archaeologists great ones search workshoutout think tech generations archaeologists like tech general public become used something one way find capacity change check video millennial trying rotary phone nothing else get laugh 0.826563226106249\n",
      "thanks response u beglium appreciate sentiment clearly honing type archaeology cultural resource management crm different folks thread really know exotic locations adventure pyramids academic archaeology crm archaeology required federal law e compliance ensure protection valuable cultural heritage like protect projects like ones seeing typically nsf like funding sources referred earlier response question good one archaeology matter think lot archaeologists tell need study past better plan future think true think bit cop really resonate folks archaeologists essential like doctors fire fighters however stories tell past archaeological record compelling important human story follow coverage around clotilda discovery links elsewhere ama past also powerful propaganda tool fact ancient civilizations using history past political means thousands years see played bit debate confederate monuments us also many organizations use archaeology cultural heritage directly better people lives either economically check sustainable preservation initiative mentally check american veterans archaeological recovery links http www sustainablepreservation org http www sustainablepreservation org https americanveteransarchaeology org https americanveteransarchaeology org sorry specific experience archaeologists say search prides seamlessly integrating construction crews also say crm company important understand needs expectations client course communication key including something saved 0.7852796441513853\n",
      "albert lin great storyteller done amazing things archaeology related projects topics however archaeologist technologist scientists tv personality worried fact archaeologists bungled sorts discoveries accidental discoveries better long right thing call professionals 0.784965590098571\n",
      "getting weeds love familiar gobelki tepe incredible link maybe firewalled 10 000 year old monumental site turkey tons controversy whether hunters gathers built especially sometimes argued unless group practices agriculture possess complexity build monumental ceremonial architecture https www nationalgeographic com magazine 2011 06 gobeki tepe https www nationalgeographic com magazine 2011 06 gobeki tepe investment architecture use tools make stone pillars find remains even hunters gathers tools however bring good point archaeologists provided small glimpse past excavate sample leaving rest future archaeologists perhaps better tech see views change new evidence revealed 0.7832360664634894\n",
      "thank congrats best wishes february 0.9999999999999998\n",
      "goosebumps congratulations 0.7112261840249717\n",
      "hugely helpful looking get back yoga routine might great christmas present us thanks much advice 0.7106585812381985\n",
      "christmas gift 0.7052214417175988\n",
      "wonderful post really made day congratulations 0.6994319906837703\n",
      "bad juju 0.9999999999999323\n",
      "bad juju 0.9999999999999323\n",
      "ohhhh okay makes sense sorta worried expression bad juju 0.8825878215032428\n",
      "bad ju ju 0.8747004802175886\n",
      "sounds like pretty shitty tracker 0.8637440867568758\n",
      "bee ftfy 0.9999999999999447\n",
      "bee 0.8963603024164231\n",
      "get aaaaamen 0.7995243707303584\n",
      "meep meep 0.793061462790364\n",
      "lmao thump oaahhooohhhh 0.7927273788580346\n",
      "morelia patzcuaro lovely hope check uruapan back mexico shame situation parts guerrero hope things improve around mexico great place good luck peru 0.9999999999999999\n",
      "seriously clicked link transported back 1998 got ta help upgrade site another note find oaxacan food best mexico followed closely food yucatan fav oaxacan dish edit werdz 0.7425192789635984\n",
      "easy imagine lots scenarios might even fun thought exercise worry extremes spend time mexico polarized mexico large place varied politics one wants closed borders 0.7241804784770995\n",
      "hey mexico thinking something like another state would recommend experience 0.7195156539518099\n",
      "really ought join mexico expat fb groups sure could bring lot business way 0.7169168430974034\n",
      "earliest would guess lsd legal therapeutic settings 1.0\n",
      "thanks giving us options read addiction recovery specialist must ask stance legalization marijuana recreational use 0.7252553313408797\n",
      "use label like ketamine used might ptsd clinic accept depression anxiety patients label psilocybin therapy session long fda approved drug prescribe instead schedule 1 0.7112027949786566\n",
      "see opening legal psychedelic centres spiritual personal development healing rather current strict healing focused emergence 0.6942152637835899\n",
      "hello wondering results marijuana ptsd study published 0.6881033886683837\n",
      "really means heard parents say kid screaming hitting makes sense mom definitely trying say thank anything little shit 1.0\n",
      "maybe naive question children kid throws tantrum long let teach freak cry scream disrupt everyone else long want whenever wherever teach deal without freakout edited add replies really interesting educational thanks everyone 0.8152167177298674\n",
      "honestly know going go well would getting taught simple self defence holds stop tracks sometimes solution violence show overwhelming force going take crap anymore put put context twin girls taught early age defend bigger kids thanks cousins mostly older bigger also brought military brats 0.8061622561800202\n",
      "good let throw fit exercise demons parents give give scream long son realize mom boss eventually edit wow people rude 0.8001262240188056\n",
      "idk love parents call kids assholes 0.7976264363757424\n"
     ]
    }
   ],
   "source": [
    "import scipy.spatial\n",
    "# Find the closest nn sentences of the corpus for each query sentence based on cosine similarity\n",
    "closest_n = 5\n",
    "\n",
    "for query, query_embedding in zip(queries, query_embeddings):\n",
    "    distances = scipy.spatial.distance.cdist([query_embedding], embeddings, \"cosine\")[0]\n",
    "    results = zip(range(len(distances)), distances)\n",
    "    results = sorted(results, key=lambda x: x[1])\n",
    "\n",
    "    for idx, distance in results[0:closest_n]:\n",
    "\n",
    "       \n",
    "        print(pre1[idx].strip(), 1-distance)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "modelk = KMeans(n_clusters=nn, max_iter=1000, random_state=22, n_init=50).fit(X=embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "clustered_sentences = [[] for i in range(nn)]\n",
    "for sentence_id, cluster_id in enumerate(modelk.labels_):\n",
    "    clustered_sentences[cluster_id].append(pre1[sentence_id])\n",
    "freq_list=[]\n",
    "for i, cluster in enumerate(clustered_sentences):\n",
    "    freqs = {}\n",
    "\n",
    "    for j in cluster:\n",
    "       \n",
    "        indx = pre1.index(j)\n",
    "        topic = df.loc[indx].topic\n",
    "        if not topic in freqs:\n",
    "            freqs[topic] = 1\n",
    "        else:\n",
    "            freqs[topic] +=1\n",
    "\n",
    "    freq_list.append(freqs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hist(freqs):\n",
    "    i = 0\n",
    "    for dic in freqs:\n",
    "        plt.figure(figsize=(10,7))\n",
    "        plt.bar(list(dic.keys()),dic.values())\n",
    "        plt.xticks(fontsize=8)\n",
    "        plt.title('Cluster '+str(i))\n",
    "        plt.show()\n",
    "        i +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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21E0AuAMjWQAAAwhZAAADCFkAAAMIWQAAAwhZAAADCFkAAAMIWQAAAwhZAAADCFkAAAMIWQAAAwhZAAADzBuyqupNVXVTVX1hqmz3qvpgVX2pX9+/l1dVvbaq1lfVFVX16JGNBwBYrhYyknV6kkNnlZ2Q5ILW2n5JLui3k+TJSfbrl+OSnLo4zQQA2LHMG7Jaaxcn+eas4sOTnNGnz0hyxFT5m9vEpUl2q6o9F6uxAAA7iq39TNaDWms3JEm/fmAv3yvJtVP1rutld1BVx1XVuqpat2HDhq1sBgDA8rTYH3yvOcraXBVba2tba6tba6tXrly5yM0AAFhaWxuybpw5Ddivb+rl1yXZZ6re3kmu3/rmAQDsmLY2ZJ2T5Jg+fUySs6fKf7N/y/CxSb49c1oRAGBnsmK+ClX1tiRrkuxRVdcleUmSU5KcVVXPSvLVJE/v1c9L8pQk65P8Z5JnDmgzAMCyN2/Iaq0dtYm7Dp6jbkty/LY2CgBgR+cX3wEABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABhCyAAAGELIAAAYQsgAABlixLTNX1TVJvpvk1iS3tNZWV9XuSd6RZFWSa5L8WmvtW9vWTACAHctijGT9fGvtwNba6n77hCQXtNb2S3JBvw0AsFMZcbrw8CRn9OkzkhwxYB0AAMvatoasluT8qrqsqo7rZQ9qrd2QJP36gXPNWFXHVdW6qlq3YcOGbWwGAMDysk2fyUry+Nba9VX1wCQfrKp/XuiMrbW1SdYmyerVq9s2tgMAYFnZppGs1tr1/fqmJO9JclCSG6tqzyTp1zdtayMBAHY0Wx2yqupeVXWfmekkv5jkC0nOSXJMr3ZMkrO3tZEAADuabTld+KAk76mqmeW8tbX2/qr6dJKzqupZSb6a5Onb3kwAgB3LVoes1tqXkzxyjvJvJDl4WxoFALCj84vvAAADCFkAAAMIWQAAAwhZAAADCFkAAAMIWQAAAwhZAAADCFkAAAMIWQAAAwhZAAADCFkAAAMIWQAAAwhZAAADCFkAAAOsWOoGAABLZ9UJ5y51ExbVNaccttRN+BEjWQAAAwhZAAADCFkAAAMIWQAAAwhZAAADCFkAAAMIWQAAAwhZAAADCFkAAAMIWQAAAwhZAAADCFkAAAP4g+idjD8CBYDtw0gWAMAARrIA2GkZ3WckI1kAAAMIWQAAAwhZAAADCFkAAAMIWQAAA+xU3y70LRIAYHsxkgUAMICQBQAwgJAFADCAkAUAMICQBQAwwE717UKAGb5tDIxmJAsAYAAhCwBgACELAGAAIQsAYAAhCwBgACELAGAAIQsAYAAhCwBggGEhq6oOraqrq2p9VZ0waj0AAMvRkJBVVbsk+ZskT05yQJKjquqAEesCAFiORo1kHZRkfWvty621/07y9iSHD1oXAMCyU621xV9o1dOSHNpae3a/fXSSx7TWnj9V57gkx/Wb/yPJ1YvekKWzR5KvL3Ujlpg+0AeJPkj0QaIPEn2Q3Ln64MGttZXzVRr1B9E1R9lGaa61tjbJ2kHrX1JVta61tnqp27GU9IE+SPRBog8SfZDog2Tn7INRpwuvS7LP1O29k1w/aF0AAMvOqJD16ST7VdVDququSY5Mcs6gdQEALDtDThe21m6pqucn+UCSXZK8qbV25Yh1LVN3ytOgW0gf6INEHyT6INEHiT5IdsI+GPLBdwCAnZ1ffAcAGEDIAgAYQMiaQ1WtqaqXTt3+sap60XZa9yXbYz3bW1UdW1V36dMnVNVeS92mrTW9f1TVG6vq1C2cf5+quqWqfnxMCxdHVa2qqidu5bwbPYd2ZH3fPXYB9S6qqhWzyv56S+pvZftmH6+OraqfmVVnQduwBet8dFV9dqHHxbn2h7nauRXt+K2tmOev+r+STJedXlWrtrUNcy17AfOvqaqTNnP/X/frLerzrbXQ/XKx9t8t0feZZy+g3m5V9Svbo03zEbIWoLX27621k5e6HcvNTGhaoGPT97fW2imtta8NadR21A+meyZ56Bb2xa8kOT3L/18QViXZKGRt4XYuWHUjlr09bKpfWmu/s73b0lo7vbV22WIsazOP96FJTtyW4+IitXOLQ1Zr7QWttVu3ZmWb6I8ftWH2shdjv57ah7a5z3ciu2VynF1yQtamPbKq/qmqPtzfQbylqu5aVf9YVRdW1VlJUlWP6Yn+Y1X1zKrapao+UFV7VdWT+jubVVX1ll7/R+9aquqsqvpIVZ1fVfddwm1Nb8+aqnrf1HY/qKouqKqLq+pdfdtW9e1/Z5JjZ29/X85FVfXyqvp0VT2rqg5KcmCSC6rq6P6ucd/+ruRtVXVev1RVPaAv/7yqOruq1ixln8zjCUkuSvLRJI9Nkqo6qareUFUfqqrXV9X/rapLq+rFU/P9fJI/SPIL273FW+a4JEf3fWD6MT+x77efrKpHJUlVPb7vAxdW1TNmFlBV962qc6rqgKpa2acvrKrX9ftPqqq/y+SbyKur6uP9/hcuxQZPtfuuff97f5InJdl1vufC1Ly/XlWv6dOX9OtD+n5waVUdMmtd0/WfXVUf7ZdHV9Xu/fl0YVW9dp42z/T1WX19s7ch/Xm3UR/PdRyqqs/1Y9Yf9XnO73X+pKr2zWTf+POqelpNjb5X1UX9eiH7w0m9nWt6O9/X57l3Vd2t13t/Vb29Hys2antN/jXkEb1/HjHV16uq6vQ+fWlVra2qdVX1SzNtrKoVNfmJoUur6r2Z/Jbjpvp1TW/L+5Ic1veBj1fVoVX1y1Nt+IWpZU/v13tU1Yv7fR+uPmJWVW+qqg8lOXpqXS/ty//rqW24ZHafb24/WKiqekVV/VRV/WJVfbaXnZHkgUn+X/Xjdy/f3P471/P6+F73wr4f368mr50Xz+zH/TF9a1Wd2+f/7X7/G/v996jJ68OHq+odVbVrX+Uv1uQ19uy+j89+3dq999XM4zHvr7IP1VpzmXVJsibJB/v0M5KckOQtSfZNcnovn/lm5geS3DeTX7n/UJK7Jnl4Jr8LdlGSe2UyIvCWqWWf1Kfv2a+fneQ5ffqSZbTdJya5R7/90kxCwaokVybZZTPbf1GSRyW5W5KLe72Lkqzo06f3vjw2yWt62RuSPDLJHyc5spf9U5I1S70/bKKfXprktUkeksnfQr2i33dSkt/r0+cneWqfXtevVyY5rU+/OcluS709C9jO2Y/5zH67b5Iz+/RHk+zRp+/S531NkrOTPLyXvzLJ4/r0nyd5XO+vE6eeB8f26Vribf+1JC/q02uTPHMBz4WLkvxGktdOLeeSmev+PLlvko/PVT+Tvxw5pz+Xdk/y3iSH5PbjxZx9Mruve58eMsc2HDtXH2fu49A3ktyrT78jyT59+m2ZBJKTkhwyvY0z27QF+8NMO9ckObuXvSiTEd5nJDmhl526mbZfMkdfr8rtx+l/TfITmRyHPzbV7yuSvC7J4/v0lUlWbaZ/39+3Y2beuya5YK7t7/eflNv360ck+ds+vX+Sv83k/33f0Mte2OvvmeS8XvaMqW24ZLq/FnEff2qS5/blnpfkPn07L8odj9+b2n9XZO7n9fm5/flSSf4wydH99huTPCYbH//XZuPj5u5JfifJUb3st5Mc1eeZ6bc/zuQ3ONfkjq/Xq9Jfc5f6YiRr0z7bry/P5ECQ1tr6JJ+vqjOT/F6//5GZHBgvTPJjSVa2yW+C3SPJB1pr/5GN/1Kokh+danpFVV2c5PlJlsvnc6a3+2FJTquqjyR5Wm5v4+fa7UPid9j+Xv6F1toPktw2z/q+0K+/lskQ70OSXDHVhuXsiZm8ALwmfR/pZrbp+qnp7/XH/PAkB9ZkdOGnkvzSdmrrtpp+zI/u++0bM7Xftta+3q9nHvOnJ7m83f4befsnOaWPdhw8Ne/MKaOzkvx0f34dOmpDFuihuf25cFkmL9LzPReSyRuTl8yxvNZa+05r7TtJNlX/oZk8ny5M8u5Mng8fSXKXqnprJoFsU2b39VzbkMzq480ch67ux65k8ibi7/vjtn+SOT9PWbXxabEF7A/TFnIcWOj+Md2Ob7TWvtq3ZfYpwocm+Wxr7ZapdW3KZzIJwftn8mby/CR7zt7mWWb6fP8ka3r/nZpJUJnrsXlwbu+H7XHs+1iS/5XJcf7MTI5NN/b7Zh+/N7X/JnM/r1+S5NSqWpvJyNjDMunDJFmXyRu0ZO5j5fVJ7t+X+4K+3GP6cpKNX6P23UzZsiBkbdojp64vSJKquluSV7fW/ncmB6gHZfLgHtZaW5PkUa21r1XVk5NcleSQqtojybczCSDJ5F1NMjl9dq/W2hOS/E3m/r/HpTC93V9J8i+ttZ9L8q7c3sbp4HSH7e/ls3+A7YeZ/DDtbLMD6Fdyex/99NZswHby6CTvaa0d2lo7NMkHqmqm3dPbNHv7Dkvys32eJ2R5h6zpx2z6MX9eJu8en5Pb94lWVQ9INvrcyt8l2buqjui3r07y+621NW3y/2Vnz1r2D1trv5/JqNGfLfK2bKmv5Pbnwsy7+vmeC8nkxeAtVXWPWeV3qcmpsvtm4+fBdP2vJPl07581mYyW7dJae3Fr7dczOcW8KbP7eq5tSO7Yx5s6Dk1v19WZjCisSfIzmfyjx7Tqx8ZHTJUtZH+YtpDjwFz7x/R8d+/X0+3Yvar2rqp75o7Hn69k8rGQXWbNM5fbMvlj488nObj3xSPbZPhkUz82OdOHVyc5f+px/c3M/dj8W5ID+vTwY19r7aZMRs9+mEng+j9JPj5z96zqm9p/k7mf15e31o7NZLTr2CRfzmTfSZLVmYwwzl7P7H3g6iR/0Zf72ExGHpONX6P+dRNlm3q92e626zcDdjA/7KMNd8/k4PbwTN5pnFaTb1R8OclNmST2c/qB5Js1+QbPH2XyYvrwJK9srR1TVV/t59/XJ/n3THagffs6rs3kHdxyML3dxyR5T1WtziQofmmO+httf5Jf3cRyz03y3pnz7ZvxxiTvqsnnu27L5MmyHH0ukwPIjIsy/wct75nkPq21/0qS1tr3+ucZ7jFTtsx8IcnLM3nXPf04fCrJxf0y48Qk76uqHyR5fSbviFsmpyPeXlXfSvKyJGur6n6ZPLbPmbW+X67JP0XcM5PT80vpvUneWVUfSPKtTEamf3me50IyeSf9iiRvrqqjpsr/LJPRj0ry4rnqZ3I65Nw+qnRrkg8n+UhVvSzJrpmMoGzKj/o6yQMyOb0zexuSO/bxQo5DL0ryph6kfpg7PsdP7+t7/1TZQvaHzXlvkn/obf9eX+9c+8e1VfWu3sZza/K5rE9OLefrmZwOOzB3DO5/meStvW03Zh6ttduq6lWZfLa0JflikuOTfKomn+t65Sbm+1xV/XsfkWlJ3tZaW9s/g3RBJuHqq621G6rq8qr6aF/29jj23ZDk8621a/pnlz6eyX4426b232Tu5/VJVfWQTN6cPDOT0am3VtVzklzRWru0qv7nPG1bm+QNVfW8vt4Te/kDqur8JN/PZHT0cdn4detXk9ycScB+Z5LjWmvfXGB/LDq/+M6P1ORD5oe01v5kCdsw8w3E26rq3EyeIMslgMKC9VDyj6215f4Fh2Wpqla0yV+0nZrkza21T2zFMi5prf3sgOYNMbXNz0jy0Nbay5e6Tcvdcnjd2hwjWSw3987kHeldk3xIwGIHdmYmI0tsnXOr6t5J1m9NwNpBnVxVj8tkJPPXlroxbDsjWQAAA/jgOwDAAEIWAMAAQhYAwABCFgDAAEIWAMAA/x9P8YWAOuqDDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist(freq_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
